Method for Time-Evolving Rectilinear Contours Representing Photo Masks

ABSTRACT

Photomask patterns are represented using contours defined by level-set functions. Given target pattern, contours are optimized such that defined photomask, when used in photolithographic process, prints wafer pattern faithful to target pattern. Optimization utilizes “merit function” for encoding aspects of photolithographic process, preferences relating to resulting pattern (e.g. restriction to rectilinear patterns), robustness against process variations, as well as restrictions imposed relating to practical and economic manufacturability of photomasks.

BACKGROUND INFORMATION

1. Field of Invention

Invention relates to masks, also known as photomasks, used inphotolithography processes and, more particularly, to a method forapplying level-set technology to time-evolve contours representingpatterns on photomasks in such a way so as to allow for production ofwafer patterns with minimal distortions or artifacts and to allow forthe ability to constrain resulting contours to rectilinear patterns.

2. Description of Related Art

Lithography processing represents an essential technology formanufacturing Integrated Circuits (IC) and Micro Electro-MechanicalSystems (MEMS). Lithographic techniques are used to define patterns,geometries, features, shapes, et al (“patterns”) onto an integratedcircuit die or semiconductor wafer or chips where the patterns aretypically defined by a set of contours, lines, boundaries, edges,curves, et al (“contours”), which generally surround, enclose, and/ordefine the boundary of the various regions which constitute a pattern.

Demand for increased density of features on dies and wafers has resultedin the design of circuits with decreasing minimum dimensions. However,due to the wave nature of light, as dimensions approach sizes comparableto the wavelength of the light used in the photolithography process, theresulting wafer patterns deviate from the corresponding photomaskpatterns and are accompanied by unwanted distortions and artifacts.

Techniques such as Optical Proximity Correction (OPC) attempt to solvethis problem by appropriate pre-distortion of the photomask pattern.However, such approaches do not consider the full spectrum of possiblephotomask patterns, and therefore result in sub-optimal designs. Theresulting patterns may not print correctly at all, or may not printrobustly. Accordingly, there is a need for a method for generating theoptimal photomask patterns which result in the robust production ofwafer patterns faithful to their target patterns.

SUMMARY OF INVENTION

Photomask patterns are represented using contours defined by level-setfunctions. Given target pattern, contours are optimized such thatdefined photomask, when used in photolithographic process, prints waferpattern faithful to target pattern.

Optimization utilizes “merit function” for encoding aspects ofphotolithographic process, preferences relating to resulting pattern(e.g. restriction to rectilinear patterns), robustness against processvariations, as well as restrictions imposed relating to practical andeconomic manufacturability of photomasks.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a diagram illustrating a simple example target pattern to beprinted on a wafer using a photolithography process, according to anembodiment of the present invention.

FIG. 2 is a diagram illustrating an example photomask pattern in the(x,y) plane comprising regions, according to a preferred embodiment ofthe present invention.

FIG. 3 is a diagram showing an example wafer pattern illustrative ofwhat might print on a wafer using the example photomask pattern of FIG.2 in a photolithography process, according to an embodiment of thepresent invention.

FIG. 4 a is a diagram illustrating a level-set function representing theexample photomask pattern of FIG. 2 by defining the contours whichenclose the regions in the photomask pattern, according to an embodimentof the present invention.

FIG. 4 b is a diagram illustrating the level-set function of FIG. 4 aintersected with the zero plane parallel to the (x,y) plane, accordingto an embodiment of the present invention.

FIG. 5 is a flow chart illustrating a method for time-evolving contoursof a photomask pattern in order to minimize a Hamiltonian function,according to a preferred embodiment of the present invention.

FIG. 6 a is a diagram illustrating a photomask pattern, according to anembodiment of the present invention.

FIG. 6 b is a diagram illustrating a photomask pattern corresponding toa final level-set function output by the algorithm, according to anembodiment of the present invention.

FIG. 6 c is a diagram illustrating a wafer pattern as produced using thephotomask pattern of FIG. 6 b in a photolithography process.

FIG. 6 d is a diagram illustrating a rectilinear photomask patternoutput by the algorithm based on the initial photomask shown in FIG. 2,according to an embodiment of the present invention.

FIG. 6 e is a diagram illustrating a wafer pattern as produced using therectilinear photomask pattern of FIG. 6 d in a photolithography process,according to an embodiment of the present invention.

FIG. 7 is a diagram illustrating a 2-dimensional sub-space of anm-dimensional solution space of level-set functions, showing HamiltonianH as a function of ψ(x₁,y₁) and ψ(x₂,y₂), according to an embodiment ofthe present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENT(S)

As understood herein, the term “wafer pattern” is understood to includeany polygon (rectilinear or non-rectilinear) or other shape or patternto be formed on a semiconductor or other material substrate, for exampledigital or analog circuit structures or interconnect.

FIG. 1 is a diagram illustrating an example target pattern 100 to beprinted on a wafer using a photolithography process. Target pattern 100comprises regions 101 enclosed by contours 102. Preferably, areas withinregions 101 represent photoresist and the area outside regions 101represents the absence of photoresist.

FIG. 2 is a diagram illustrating an example photomask pattern 201 in the(x,y) plane comprising regions 201 for printing a wafer pattern in aphotolithography process. In a preferred embodiment, an area within aregion 201 represents chrome and the area outside regions 201 representsglass on the photomask. Alternatively, an area within a region 201represents a material other than chrome and the area outside regions 201represents a material other than glass on the photomask.

FIG. 3 is a diagram showing an example wafer pattern 300 illustrative ofwhat might print on a wafer using photomask pattern 200 in aphotolithography process. Preferably, areas within regions 301 representphotoresist and the area outside regions 301 represents the absence ofphotoresist. Note that wafer pattern 300 differs from target pattern 100due to distortions and artifacts produced by the photolithographyprocess. It is an object of the present invention to generate aphotomask pattern which, when used in a photolithography process,produces a wafer pattern more faithful to the corresponding targetpattern, the wafer pattern having fewer undesirable distortions andartifacts.

Because we use contours to define regions in a photomask pattern, we usea mathematical description of such contours. FIG. 4 a illustrates alevel-set function ψ(x,y) 400 representing example photomask pattern 200by defining the contours which enclose the regions in photomask pattern200. ψ(x,y) is a function with the property that

-   -   1. ψ(x,y)=0 everywhere along the boundary of a region;    -   2. ψ(x,y)>0 “inside” a region (for example, those regions        corresponding to the chrome portions of the mask);    -   3. ψ(x,y)<0, or is negative “outside” a region (for example,        those regions corresponding to the clear quartz portions of the        mask).        The contours are defined by the “level-set”, i.e. those values        in the (x,y) plane such that ψ(x,y)=0. FIG. 4 b illustrates the        level-set by intersecting the level-set function 400 with the        zero plane 401 parallel to the (x,y) plane.

It is an aspect of the present invention to, given a target pattern,find a level-set function ψ(x,y) such that the level-set ψ(x,y)=0defines a set of contours, which, when interpreted as the boundaries ofregions of a pattern on a photomask, correspond to the design of aphotomask producing a wafer pattern with little distortions andartifacts compared to the target pattern, wherein the wafer patternresults from a photolithography process using the photomask. The extentto which the set of contours defined by a level-set function ψ(x,y) isoptimal is calculated using a functional known as the “merit function”,also referred to herein as the “Hamiltonian” H. The Hamiltonian H of alevel-set function ψ(x,y) is indicative of the degree of similaritybetween the printed wafer pattern and the desired target pattern, theprinted wafer pattern resulting from a photolithography process using aphotomask given by the contours defined by ψ(x,y). (We call the “meritfunction” the “Hamiltonian” by way of analogy to the Hamiltonianfunction used in classical dynamics or quantum mechanics).

Mathematically, the Hamiltonian H is a functional which maps a functionto a real number:

H:C(

²)→

Optionally, the Hamiltonian also depends upon a number of realparameters, or, as described below, is a function of multiplelevel-sets. The Hamiltonian is chosen so that the optimal solution hasthe smallest value, i.e. we seek to find a level-set function whichminimizes the Hamiltonian. It follows that, once an appropriateHamiltonian is specified, the problem of finding an optimally designedphotomask is equivalent to the problem of finding a level-set functionwhich minimizes the Hamiltonian. It also follows that the specificationof an appropriate Hamiltonian is a valuable step in applying theprinciples of our invention, given that the form of the Hamiltonianfunctional will directly determine the contours which result from theoptimization problem.

Note that our description of the problem in terms of minimizing aHamiltonian function is for purposes of description and not by way oflimitation. Equivalent alternatives are available to one of ordinaryskill in the art based on the presented description (for exampleformulating the optimization problem as a maximization problem ratherthan a minimization problem, or assigning negative values to thelevel-set function values at points on the inside of enclosed regionsand assigning positive values to the points on the outside of enclosedregions, or by choosing a level-set other than the zero level-set tospecify contours, etc.).

FIG. 5 is a flow chart illustrating a method for time-evolving contoursof a photomask pattern in order to minimize a given Hamiltonianfunction, according to a preferred embodiment of the present invention.FIG. 5 depicts the steps used to find a level-set function which definesan optimal photomask for a given target pattern. The level-set functionis found by iteratively refining an initial guess so that therefinements progressively result in better “merit” values, i.e. decreasea Hamiltonian H, wherein H depends on the target pattern, the particularphotolithography process under consideration, constraints on thephotomask manufacturing, and other factors to be described in detailbelow. We briefly describe the steps in FIG. 5 prior to providing thedetail.

Start 501 with a set of initial inputs, which may include a targetpattern, parameters describing the particular photolithography processunder consideration, mask manufacturing restrictions, and other factorsto be described in detail below. Initialize 502 i=0 and choose 503 aninitial level-set function ψ_(i) (x,y)=ψ(x,y). Determine 504 whetherψ_(i)(x,y) is acceptable (details on determining this below). Ifψ_(i)(x,y) is 505 determined to be acceptable, output 506 ψ_(i)(x,y) asthe result of the minimization, and finish. Otherwise 507, increment 508i by one and choose 509 a next ψ_(i)(x,y) so as to gain an improvement(details on choosing next ψ_(i) appear below), repeating untilψ_(i)(x,y) is determined 507 to have acceptable “merit”, and finishingby outputting 506 the final ψ_(i)(x,y) as the result of theminimization. Because the initial level-set function ψ₀ changes througheach iteration, it can be thought of as evolving with time, and it isconvenient to think of each successive function ψ_(i)(x,y) as a discretesnapshot of a continuously evolving “time-dependent level-set function”ψ(x,y,t) of space and time (t denoting time).

FIG. 6 a illustrates a photomask pattern 602 corresponding to alevel-set function ψ_(i)(x,y) after about 500 iterations of thealgorithm. FIG. 6 b illustrates a photomask pattern 603 corresponding toa final level-set function output by above optimization algorithm. FIG.6 c illustrates a wafer pattern 604 as produced using photomask pattern603 of FIG. 6 b in a photolithography process.

In one embodiment, a succeeding function ψ_(i+1)(x,y) is chosen byadding a small change Δ_(i)(x,y) to the current level-set functionψ_(i)(x,y), wherein Δ_(i)(x,y) is another function over the same domainas ψ_(i)(x,y):

ψ_(i+1)(x,y)=ψ_(i)(x,y)+Δ_(i)(x,y)  (1)

In a preferred embodiment, a succeeding function ψ_(i+1)(x,y) is chosenby first adding a small change Δ_(i)(x,y) to the current level-setfunction ψ_(i)(x,y) and then projecting the resulting sum onto asub-space which constrains the contours to be rectilinear (details onthe projection appear below). FIG. 6 d illustrates a rectilinearphotomask pattern 605 output by above algorithm based on the initialphotomask 200 shown in FIG. 2. FIG. 6 e illustrates a wafer pattern 606as produced using rectilinear photomask pattern 605 of FIG. 6 d in aphotolithography process.In one embodiment of our invention, we calculate Δ_(i)(x,y) as follows:

$\begin{matrix}{{\Delta_{i}\left( {x,y} \right)} = {\Delta \; {t \cdot \left\{ {\frac{\delta \; H}{\delta\psi}_{\psi = \psi_{1}}{+ {R\left( \psi_{i} \right)}}} \right\} \cdot {{\nabla\psi_{i}}}}}} & (2)\end{matrix}$

where Δt is a small constant hereinafter referred to as “time-step”,δH/δψ is the Frechet derivative of the Hamiltonian H, R(ψ) is a“regularization term” which slightly modifies the evolution of thelevel-set function to improve numerical stability, and |∇ψ_(i)| is thenorm of the gradient of the level-set function ψ_(i)(x,y). Each of theseterms as well as the projection operation will be described in moredetail below.

In still another embodiment of our invention, we use the continuous-timeversion of equation (2) above and time-evolve the time-dependentlevel-set function according to the equation

$\begin{matrix}{{\frac{\partial}{\partial t}{\psi \left( {x,y,t} \right)}} = {\left\{ {\frac{\delta \; H}{\delta\psi} + {R(\psi)}} \right\} \cdot {{\nabla\psi}}}} & (3)\end{matrix}$

which can be implemented computationally using a variety of techniquesdifferent from the discretization described in equation (2) above, butthat are known to one of ordinary skill in the art.

In a preferred embodiment, and to facilitate computation, a level-setfunction ψ_(i)(x,y) is represented by a discrete set of m functionvalues over a set of m points in the (x,y) plane. In one embodiment, theset of m points comprises a grid spanning an area representing thephotomask. Alternatively, the set of m points is chosen according to adifferent arrangement in the area representing the photomask. From thisperspective, a level-set function ψ_(i)(x,y) and a “small change”function Δ_(i)(x,y) are determined by their values at the set of mpoints and consequently can be considered as m dimensional vectors in“solution space.” FIG. 7 is an illustration of the possible values forthe first two components of an m-dimensional vector representing alevel-set function, i.e., illustrating a 2-dimensional sub-space ofsolution space. In the subspace shown, we plot H as a function ofψ(x₁,y₁) and ψ(x₂,y₂). For this example, both ψ(x₁,y₁) and ψ(x₂,y₂) canvary between −1 and +1. The minimum in this example is seen to occur atψ(x₁,y₁)=0.3 and ψ(x₂,y₂)=−0.2. Starting with an initial guess at alevel-set function ψ₀(x,y), we approach the minimum by taking a smallstep (in step 509) in the direction of “steepest descent” to obtain anew location which is closer to a minimum. By repeating this process wequickly reach a minimum. Time-evolving the level-set function accordingto above preferred embodiment is analogous to the foregoing, except thatthe dimensionality of the entire “solution space” is much greater than 2(for example, it can equal the number of grid points m in thediscretized version, or be infinite in a continuous version).

From the above discussion, it is seen that one can find the minimumwithout actually calculating the Hamiltonian. However, it may be usefulto calculate the Hamiltonian in order to determine the “merit” of thecurrent level-set function. For example, it may be reasonable to stopiterating, even before the algorithm converges on an optimal solution,if a sufficient solution has been found. Similarly, one may wish tocheck the Hamiltonian occasionally (every several iterations), or onlyat those times when an adequate solution seems likely to have beenfound, such as when the level-set evolution generates only small changesin the succeeding level-sets.

At this point we shall reexamine the steps of the flow chart of FIG. 5in more detail:

Input

The algorithm starts 501 with a set of inputs, among them a targetpattern given in a particular format (“pattern I/O formats”). The targetpattern may be presented in a variety of formats, including, but notlimited to:

-   -   1. Image formats, such as bitmaps, JPEGs (Joint Photographic        Experts Group), or other image formats;    -   2. Semiconductor industry formats, such as CIF, GDSII, Oasis,        OpenAccess; or    -   3. Proprietary formats, such as an Electronic Design Automation        (EDA) layout database.        The target pattern itself may be a representation of various        types of content, for example (but not limited to):    -   1. One or more levels of an IC design for a particular IC type;    -   2. A pattern for a non-IC application (e.g. a MEMs device);    -   3. A pattern which can be used as part of a larger design, such        as a standard cell, or DRAM bit cell, etc.        The algorithm also accepts one or more constraints as input,        including (but not limited to) target pattern or mask pattern        constraints, which may be specified as rules (such as critical        dimensions, tolerances, etc.); and target pattern or mask        pattern constraints specified as additional images (in a        “pattern I/O format”) specifying for example maximal or minimal        areas to be covered, or critical regions, etc.

It is contemplated that the teachings of the present invention can beused to refine a photomask pattern determined through some otherprocess, and that the output of the algorithm of present invention couldbe fed, or otherwise used, as input into another technique ormethodology for optimally providing a photomask. In general, aniterative process is contemplated in which a mask pattern is takenthrough a series of transformations, with the teachings of the presentinvention accomplishing a subset of those transformations.

Other accepted inputs include parameters of the Hamiltonian function,including but not limited to parameters of the physical model used tosimulate the photolithographic process, and parameters indicating thedesired attributes of the eventual solution. These may include, forexample, the number and type of photomasks employed, the wavelength of astepper machine, the type and properties of the illumination, the typeand properties of the photoresist, the type and properties of the lens,etc. Other parameters may include properties of error sources, such asdefocus, exposure, alignment, defects, etc.

Initialization

After receiving inputs in step 501 and initializing 502 i=0, weinitialize 503 the level-set function ψ₀. In theory, almost anylevel-set initialization should be sufficient; however, initialconditions can have an impact on the time required for convergence andtherefore on the cost of the process. Moreover, it is possible that forsufficiently poor initial conditions the algorithm will fail toconverge.

A variety of ways to initialize the level-set function will be apparentto one of ordinary skill in the art. In one embodiment of the presentinvention, the initial level-set function is chosen, according to aninitial photomask pattern comprising enclosed regions (to be chosenbelow), by assigning

-   -   1. the value +1 to ψ₀(x,y) everywhere within the enclosed        regions of the photomask pattern;    -   2. the value −1 to ψ₀(x,y) everywhere outside the enclosed        regions of the photomask pattern; and    -   3. the value 0 to ψ₀(x,y) at the boundaries (contours) of the        regions of the photomask pattern.

However, it is desirable to have a smoother and approximately continuousfunction as the level-set function. In a preferred embodiment of thepresent invention, the level-set function is a “distance function,”wherein the value of the function at a given point represents the(signed) distance of the point to the (nearest) boundary of a region inphotomask pattern (positive inside a region's boundary, negativeoutside). Such a distance function has a variety of useful properties.For example, in the context of the present invention, a distancefunction allows for computations that depend not just on what is insidea region's boundary or outside a region's boundary, but what is “near”the boundary, where “near” is functionally based on distance. As thelevel-set function evolves, it slowly loses its property as a distancefunction. However, this can be corrected using a “re-distancing”process, known to one of ordinary skill in the art.

It remains to determine an initial photomask pattern on which to basethe choice of the initial level-set function ψ₀(x,y) in step 503. Avariety of possible choices are available including (but not limited to)the following:

-   -   1. Random. This is unlikely to be the choice leading to fastest        minimization, but it should be very robust;    -   2. The target pattern. Especially for the case of a single        chrome, and glass mask, choosing the initial mask pattern to be        equal to the target pattern is likely to perform fairly well.        This is because it is possible for the final mask pattern to be        similar to the target pattern;    -   3. The result of heuristics applied to the target pattern. For        example, an OPC algorithm is applied to the target pattern, with        the result used as the initial mask pattern. For multiple mask        processes, one approach is to use heuristics to split the        pattern into multiple exposures, for example, separating        horizontal and vertical lines;    -   4. Results from previous solutions to the same or similar        problems. These are likely to be similar to the desired final        pattern; or    -   5. Results from solutions to other similar regions on the mask.        As above, these are likely to yield similar solutions. One can        imagine, for example, that the mask comprises individual mask        areas. As pattern on such individual areas are optimized, the        solutions can be used as initial guesses for other areas. Since        the optimal solution for a given area will depend upon        interactions with neighboring areas, the solutions may not be        the same. However, the optimal solution from one area may serve        as a good initial guess for another similar area.

There are numerous ways to initialize an original target photomaskpattern. The previous possibilities that have been described are meantonly as a partial list of possible alternatives.

In one embodiment, a level-set function is stored as an array of valuesrepresenting the value of the level-set function at fixed points on a2-dimensional grid. Optionally, a more sophisticated approach (referredto as “local level-set”) only stores the values near the boundaries;depending upon the pattern and the resolution, this can be significantlymore efficient. Other ways of representing and storing a level-setfunction will be apparent to one of ordinary skill in the art.

Checking the “Merit”

As seen in the flow chart, in step 504 the algorithm determines if ithas converged upon a suitable set of contours so as to provide anoptimal photomask. In one embodiment, this check is performed after eachstep in the evolution of the contours (defined by the level-sets).Alternatively, this check is performed after two or more steps of thelevel-set evolution.

One simple method to determine whether an acceptable solution has beenfound (in step 504) is to calculate the value of the HamiltonianH(ψ_(i)) resulting in a “merit” of the current level-set solution.Alternatively, a solution is deemed acceptable based on the number ofiterations performed. It may be advantageous to use locally definedcriteria to stop iterating in areas of the photomask where the solutionis already acceptable, and to continue iterating in areas where thesolution has not yet reached an acceptable level of merit. In thiscontext, “local” can mean on the level of a pixel, on the level of asmall area (for example, an interaction distance), on the level of ahierarchical subdivision of the mask area, or on other alternativelevels.

Yet another indication of convergence is provided by the magnitude ofthe gradient (in “solution space”) or Frechet derivative—as the contoursapproach an optimal state, the derivative decreases and approaches zero.Similarly, the change in the shape of the contours from one iteration toanother iteration provides an indicator of convergence. Although we havedescribed several indicators, one of ordinary skill in the art willrecognize other indicators as well.

Time-Evolving Contours

As described above, in a preferred embodiment a level-set functionevolves in a series of steps in which we add to it a small functionΔ_(n)(x,y) calculated via equation (2)

${\Delta_{i}\left( {x,y} \right)} = {\Delta \; {t \cdot \left\{ {\frac{\delta \; H}{\delta\psi}_{\psi = \psi_{1}}{+ {R\left( \psi_{i} \right)}}} \right\} \cdot {{\nabla\psi_{i}}}}}$

It is common for the optimization problem of the present invention to bemathematically “ill-posed” in the sense that the solution may benon-unique. In order to avoid inherent numerical instabilities duringthe time evolution we employ a “regularization” technique, adding asmall term R(ψ) to the Hamiltonian H in order to help stabilize the timeevolution. The resulting contours will have less “noise” and appearsmoother to the eye. There are many ways to add regularization whichwill be apparent to those skilled in the art, including (but not limitedto):

$\begin{matrix}{{R(\psi)} = {\nabla{\cdot {\frac{\nabla\psi}{{\nabla\psi}}.}}}} & 1\end{matrix}$

-   -   Mean curvature regularization —Adding this term tends to reduce        noise in the image by minimizing the length of contours.

$\begin{matrix}{{R(\psi)} = {{\nabla{\cdot \frac{\nabla\psi}{{\nabla\psi}}}} - {\overset{\_}{\nabla{\cdot \frac{\nabla\psi}{{\nabla\psi}}}}\mspace{14mu} \left( {{with}\mspace{14mu} {the}\mspace{14mu} {bar}\mspace{14mu} {indicating}\mspace{14mu} {average}} \right).}}} & 2.\end{matrix}$

-   -   Average mean curvature—This tends to minimize the length of the        boundaries while keeping the total area of the enclosed regions        fixed, giving preference to smoother contours and contours        enclosing larger regions, since larger regions have less        boundary per unit area as compared to many small regions.

$\begin{matrix}{{R(\psi)} = {{\frac{\partial}{\partial x}\left( \frac{\psi_{x}}{\psi_{x}} \right)} + {\frac{\partial}{\partial y}{\left( \frac{\psi_{y}}{\psi_{y}} \right).}}}} & 3\end{matrix}$

-   -   Wulf-crystal regularization or Wulf curvature—This is similar to        curvature except that it prefers Manhattan geometries. Other        variations of Wulf regularization can be used preferring        straight edges in Manhattan geometries or 45 degree angles. Use        of Wulf-crystal regularization may be helpful in the design of        masks with rectilinear contours, although it will not guarantee        rectilinear geometries.

$\begin{matrix}{{R(\psi)} = {{\frac{\partial}{\partial x}\left( \frac{\psi_{x}}{\psi_{x}} \right)} + {\frac{\partial}{\partial y}\left( \frac{\psi_{y}}{\psi_{y}} \right)} - {\frac{\partial}{\partial x}\left( \frac{\psi_{x}}{\psi_{x}} \right)} + {\frac{\partial}{\partial y}{\left( \frac{\psi_{y}}{\psi_{y}} \right).}}}} & 4\end{matrix}$

-   -   Average Wulf curvature—Combining aspects of average mean        curvature and Wulf curvature, this exhibits a preference for        rectilinear contours enclosing large regions.        In all of the above regularization expressions, it is possible        for the denominator in one or more of the fractions to equal        zero. In order to avoid dividing by zero, one can add a small        constant to the denominator, or set the expression equal to zero        whenever both the numerator and denominator equal zero.

One of ordinary skill in the art will recognize other possibilities forregularization. It should be obvious that it may be desirable for theamount or type of regularization to change with time as the contoursevolve, and that alternative ways of introducing regularization areapparent to those skilled in the art and are part of the teachings ofthe present invention.

In equation (2), as well as in several of the regularizationexpressions, we need to compute |∇ψ|. The way in which the gradient iscomputed can have important consequences in terms of numericalstability. Preferably, techniques for calculating gradients known tothose skilled in the art as Hamilton-Jacobi Essentially Non-Oscillatory(ENO) schemes or Hamilton-JacobiWeighted Essentially Non-Oscillatory(WENO) schemes are used. Alternatively, other ways of computing thegradient are used, as are known to one of ordinary skill in the art.

In a similar vein, the time evolution of the time-dependent level-setfunction can be implemented using a variety of numerical techniques. Oneembodiment described above uses what is known as “first order RungeKutta”. Alternatively, other variations such as third order Runge Kuttacan be used as will be obvious to those skilled in the art.

The method of gradient descent involves multiple iterations; the size ofthe function Δ_(i)(x,y) chosen as part of performing step 509 is scaledby the “time-step” Δt appearing in equation (2). The larger thetime-step, the faster the system converges, as long as the time-step isnot so large so as to step over the minimum or lead to numericalinstabilities. The convergence speed of the algorithm can be improved bychoosing an appropriate time-step.

There are numerous ways to choose the time-step Δt. In one embodiment,we choose a time step that is just small enough so as to guarantee thatthe system is stable. In an alternative embodiment, we start with alarge time step and gradually reduce the time step as the algorithmapproaches a minimum. In an alternative embodiment, we vary the timestep locally and per sub-area of the photomask. Other approaches will beknown to those skilled in the art, or other means of adapting the timestep to the specific situation.

In another embodiment, one can use what are known as implicit methods,optionally with linear-preconditioning, in order to allow for a largertime-step. Still other variations will be known to one of ordinary skillin the art.

Analogous to refining the time granularity by reducing the time-step, inone embodiment of the present invention the granularity or placement ofthe grid of points on the photomask is adjusted in a time-dependentfashion as the algorithm approaches convergence. By performing theinitial iterations on a larger grid, and increasing the number of gridpoints with time as greater accuracy is desired, a solution is obtainedmore quickly. Other such “multi-grid” techniques will be apparent tothose skilled in the art. Another possibility is using an adaptive meshtechnique, whereby the grid size varies locally.

It is possible that the process of time-evolving the contours arrives ata configuration from which there is no “downhill” path in“solution-space” to a solution, or in which such paths are inordinatelylong or circuitous. In such a state, convergence may require a largenumber of iterations. Also, the algorithm may get “stuck” at a local(but not global) minimum. Some example techniques for handling such asituation are as follows:

-   -   1. Changing the Hamiltonian. Various modifications can be made        to the Hamiltonian in order to bridge local minima in solution        space; for example, regularization terms can sometimes be used        for this purpose;    -   2. Addition of random bubbles. Adding random noise to a        level-set function will create new regions, which can then        time-evolve into the solution. Noise (that is distortion) can be        purposefully added at random or it can be targeted in specific        regions (for example, known problematic target geometries, or        regions which are not converging on their own to acceptable        errors, etc.);    -   3. Heuristic bubbles. Rather than adding random noise, a        specific modification feature, known from experience to        generally help the system converge, is added; for example, if        certain areas appear to be evolving too slowly, one could add a        constant to the level-set function in that area, thereby making        all the features in that area “bigger”;    -   4. Uphill steps. By making uphill moves, either at random, or        specifically in places, the algorithm avoids getting stuck in        local minima and works toward a global minimum. Similar        techniques from discrete optimization or simulated annealing        which are useful to the algorithm of the present invention will        be apparent to one of ordinary skill in the art.        Alternatives to the previous example techniques will be apparent        to those of ordinary skill in the art.

Projection Operator

In many cases, it is desired to constrain the solution to rectilinearcontours, for example to improve manufacturability or reduce costs ofthe corresponding masks. The present invention enforces this constraintby using a projection operator.

The projection operator can be understood by considering thesolution-space of all possible contours, and recognizing that the set ofrectilinear contours is a sub-space of the solution-space. Theprojection operator constrains the evolution of the contours to withinthe sub-space of rectilinear contours.

The projection operator takes a set of possibly curvilinear contours andapproximates them with a set of rectilinear contours. In one embodiment,choose a fixed grid size (possibly corresponding to manufacturingcapabilities), and “round” every contour to the nearest grid. This isaccomplished, for example, by setting the level-set function to bepositive if the majority of the points within a single grid-cell arepositive, negative if the majority of the points within a singlegrid-cell are negative, and zero along the boundaries. Alternativeimplementations of the projection operator will be apparent to one ofordinary skill in the art.

In one embodiment of the present invention, the projection operator isapplied to the level-set function after each time-step iteration. Inthis way, the contours are always constrained to be rectilinear. In analternative embodiment, the contours are allowed to evolve for multipletime-steps between applications of the projection operator, in whichcase the contours may temporarily deviate from strict rectilinear form.The preferred frequency of projection will depend upon factors such asspeed of computation, choice (or implementation) of the projectionoperator, or the manner (or implementation) of the curvilineartime-evolution.

Merit Function/Hamiltonian

As illustrated, the optimization problem and the resulting contours aredetermined by a merit function referred to as the Hamiltonian. There aremany alternative ways to choose a merit function within the scope of thepresent invention. In one embodiment, the Hamiltonian comprises a sum oftwo parts:

-   -   I. A first part, based upon the level-set contours themselves;        and    -   2. A second part, based upon the resulting pattern which would        be printed on a wafer or die given the photomask corresponding        to the level-set contours.

The first part of the Hamiltonian may comprise one or more terms suchthat the resulting optimized photomask pattern has properties which aredesirable from a manufacturing point of view; for example, the“regularization” terms described above can be viewed as elements of theHamiltonian that exhibit a preference for contours corresponding to moreeasily manufacturable masks.

The second part of the Hamiltonian takes into consideration a model ofthe photolithographic process, i.e. a method for calculating the waferpattern resulting from a particular mask pattern (a “forward model”).Following describes an example forward model for one embodiment of thepresent invention.

In a typical photolithographic process, light passes through thephotomask and the lens, and then falls upon the wafer, where thephotoresist is exposed. For coherent illumination, the electric fieldfalling upon the photomask is approximately constant. The clear regionsof the mask pass the light, while the opaque regions block the light. Itfollows that the electric field, just behind the mask, can be writtenas:

${M\left( \overset{->}{r} \right)} = \begin{Bmatrix}0 & {chrome} \\1 & {glass}\end{Bmatrix}$

where {right arrow over (r)}=(x,y) i is a point on the (x,y) plane.Corresponding to an embodiment of the present invention wherein theregions in which the level-set function is positive indicate glass andthe regions in which the level-set function is negative indicate chrome(with the level-set equal to zero at the boundaries or contours), onecan write the previous expression as a function of a level-set functionψ(x,y) as follows:

M({right arrow over (r)})=ĥ(ψ(x,y))

wherein ĥ is the Heaviside function:

${\hat{h}(x)} = \begin{Bmatrix}1 & {x \geq 0} \\0 & {x < 0}\end{Bmatrix}$

Because an ideal diffraction limited lens acts as a low-pass filter,this will serve as a good approximation to the actual (almost but notquite perfect) lens used in a typical photolithographic process.Mathematically, the action of the lens would then be written as follows:

A({right arrow over (r)})=f ⁻¹(

(f(M({right arrow over (r)}))))

where A({right arrow over (r)}) indicates the electric fielddistribution on the wafer, f indicates a Fourier transform, f⁻¹indicates an inverse Fourier transform, and

indicates the pupil cutoff function, which is zero for frequencieslarger than a threshold determined by the numerical aperture of thelens, and one otherwise:

${\overset{\Cap}{C}\left( {k_{x},k_{y}} \right)} = {{\hat{h}\left( {k_{\max}^{2} - \left\lbrack {k_{x}^{2} + k_{y}^{2}} \right\rbrack} \right)} = \begin{Bmatrix}0 & {{k_{x}^{2} + k_{y}^{2}} \geq k_{\max}^{2}} \\1 & {{k_{x}^{2} + k_{y}^{2}} < k_{\max}^{2}}\end{Bmatrix}}$

wherein k_(x), k_(y), and k _(max) represent frequency coordinates inFourier space.Finally, we determine the image in the photoresist upon the wafer. Inone embodiment this process is modeled using a “threshold resist”: inregions where the intensity is greater than a given threshold (which weshall call I_(th)), the resist is considered exposed; in regions belowthe threshold, the resist is considered unexposed. Mathematically, thisis handled once again with a Heaviside function:

I({right arrow over (r)})=ĥ(|A({right arrow over (r)})|² −I _(th))

Combining the above, we find that:

F(ψ(x,y))={circumflex over (h)}(|f ⁻¹(

(f(ĥ(ψ(x,y)))))|² −I _(th))

This is a self-contained formula which reveals the wafer patterncorresponding to the photomask pattern defined by the level-setfunction, within the context of the model just described. It should beemphasized that this is just one particular possible forward model thatcan be used within the scope of our invention, chosen by way of exampledue to its relative simplicity. More sophisticated forward models alsofall within the scope of the present invention. Such models would takeinto account, by way of example but not limitation, multiple exposures,various illumination conditions (e.g., off-axis, incoherent), the actualelectromagnetics of the light field interacting with the photomask,various types of photomasks besides chrome on glass (e.g., attenuatedphase shifting, strong phase shifting, other materials, etc.), thepolarization of the light field, the actual properties of the lens (suchas aberrations), and a more sophisticated model of the resist (e.g.,diffusion within the resist), such as a variable threshold model, lumpedparameter model, or a fully three dimensional first principles model.

Because the inverse algorithm requires many iterations of the forwardalgorithm, the latter preferably is implemented efficiently. However, asa general rule, more sophisticated models are likely to run slower thansimpler models. One embodiment of the present invention compensates forthis difference in model speed by beginning with a simpler model andthen gradually introducing more sophisticated models as the processconverges, thereby postponing the full complexity until the lastiterations. In an alternative embodiment, switching between differentmodels at different time steps obtains an averaging effect. For example,this represents an efficient way to explore the space oferror-parameters. Other variations will be apparent to one of ordinaryskill in the art.

In one embodiment, the Hamiltonian compares the pattern resulting fromthe forward model with the target pattern in order to determine thefigure of merit. For example, an L₂-norm may be calculated:

H((ψ(x,y))=|F(ψ(x,y))−T(x,y)|²

wherein T(x,y) indicates the target pattern. The L₂-norm is indicativeof the area of the non-overlapping regions of the two patterns. Thismetric approaches zero as the two patterns converge. Other examples ofdetermining a figure of merit are as follows:

-   -   1. Other Norms. These might include a cubic or other polynomial        functions of the differences;    -   2. Level-set differences. By representing a resulting pattern as        a level-set function one can calculate the distance between the        boundaries, integrated over the length of the boundaries;    -   3. Local variations. Different parts of the image may have        different degrees of importance when it comes to variations from        the target pattern. For example, gates generally need to be much        more accurately printed than interconnects. In one embodiment, a        weighting function assigns more weight to non-overlapping areas        in the portions of the design having higher accuracy        requirements. A related approach gives priority to a measure of        distances between curve, or to other metrics; or    -   4. Semantics. Certain types of errors are considered more        significant than other types. For example, within small        tolerances, variations from the target pattern are irrelevant,        whereas variations outside some tolerances are fatal, taking        into account the intent of the design and not just the geometry        of the design. In one embodiment, use local weightings to        account for errors. As an example, consider a gate which must be        printed within specific tolerances. Then the weighting factor        becomes large for points outside the tolerances. Within        tolerances, the weighting factor would be smaller, and        optionally still nonzero (so that the algorithm still prefers        designs that are closer to the target). Other ways of        incorporating design semantics into the merit function will be        apparent to one of ordinary skill in the art.

Optionally, an adjustment is provided to the Hamiltonian according toempirical measurements of an actual fabrication process.

Output

The flow chart shown in FIG. 5 ends with an output of the resultingcontours, representing a mask suitable for one of the potentialphotolithography applications and conforming to the specifications andconstraints specified in a suitable “pattern I/O format.”

Other outputs in addition to the photomask pattern corresponding to theoptimized contours are contemplated. In one embodiment, the final valueof the Hamiltonian function is output to indicate the merit of thesolution, optionally to be interpreted as a probability estimate thatthe resulting process will print within specification.

Generalizations

Foregoing discussion frequently considers a single level-set functionrepresenting contours on a single mask, the interior of those contourscorresponding to chrome regions, and the exterior corresponding to glassregions. However, in many cases it will be desirable to find contoursseparating multiple types of regions on the same mask, for example,chrome, glass, and phase-shifted glass, and/or either alternatively orsimultaneously to find contours corresponding to boundaries of regionson multiple masks, to be used in a multiple exposure process. Bothgeneralization fall within the teachings of our invention.

To allow for multiple masks it suffices to simultaneously optimizemultiple level-set functions, an algorithm for which follows directlyfrom above discussions: each level-set function time-evolves accordingto an equation analogous to (2), except that the terms on the right handside now depend upon multiple level-set functions, instead of just onone.

One can easily allow for multiple types of regions in a similar mannerto the way in which one handles multiple masks, i.e., with multiplelevel-set functions. However, with multiple-types of regions on the samemask, one must prevent regions from overlapping. Consider an examplewhere glass regions correspond to those areas in which the firstlevel-set function is positive, phase-shifted regions correspond tothose areas in which the second level-set function is positive, andchrome regions correspond to those areas in which both level-setfunctions are negative. Prohibiting the possibility for the same regionto be both clear glass and phase-shifted glass, add a constraint whichprevents both level-sets from being positive in the same area, forexample by adding a “penalty” term to the Hamiltonian, the penalty termtaking on a very large value whenever the two level-sets overlap. Thus,as the system time-evolves, the contours move so as to remainnon-overlapping. It should be apparent that this concept can be extendedin a trivial manner to more than two level sets and more than threetypes of regions. Alternatively, one can allow both level-sets to evolvefreely, and assign precedence to one of them, e.g., if both level setsare positive, define the region as clear glass. Other means ofrepresenting multiple regions (also called “multi-phase flow” in theliterature) will be apparent to those skilled in the art, and fallwithin the scope of our invention.

Similarly, while the foregoing discussion refers typically to a maskconsisting of only chrome and glass regions, these types of regionsshould not be construed to limit the applicability of the presentinvention, which is useful for any number of different types of regions.By way of example (but not limitation), phase-shifted regions, regionscovered with materials other than chrome (e.g., in an attenuated phaseshifting mask), and half-tone regions would all be within the teachingsof the present invention.

In still another embodiment, a level-set function can be used torepresent the pattern of the illumination optics; as in the foregoingdiscussion of multiple masks, this level-set function can be optimizedsimultaneously with those representing one or more photomasks. In yetanother embodiment, various parameters of the Hamiltonian can beoptimized simultaneously with one or more level-set functions, in ananalogous manner.

Accordingly, while there have been shown and described above variousalternative embodiments of systems and methods of operation for thepurpose of enabling a person of ordinary skill in the art to make anduse the invention, it should be appreciated that the invention is notlimited thereto. Accordingly, any modifications, variations orequivalent arrangements within the scope of the attached claims shouldbe considered to be within the scope of the invention.

In addition, the foregoing description of the principles of ourinvention is by way of illustration only and not by way of limitation.For example, although several illustrative embodiments of methodologiesin accordance with the principles of our invention have been shown anddescribed, other alternative embodiments are possible and would be clearto one skilled in the art upon an understanding of the principles of ourinvention. For example, several alternatives have been described forvarious steps described in this specification. It should be understoodthat one alternative is not disjoint from another alternative and thatcombinations of the alternatives may be employed in practicing thesubject matter of the claims of this disclosure. Certainly theprinciples of our invention have utility apart from making photomasksfor integrated circuits, some of which we have already mentioned.Accordingly, the scope of our invention is to be limited only by theappended claims.

Foregoing described embodiments of the invention are provided asillustrations and descriptions. They are not intended to limit theinvention to precise form described. In particular, it is contemplatedthat functional implementation of invention described herein may beimplemented equivalently in hardware, software, firmware, and/or otheravailable functional components or building blocks. Other variations andembodiments are possible in light of above teachings, and it is thusintended that the scope of invention not be limited by this DetailedDescription, but rather by Claims following.

1-16. (canceled)
 17. A computer-implemented method for determining amask pattern to be used on a photo-mask in a photolithographic process,wherein the photo-mask has a plurality of distinct types of regionshaving distinct optical properties, comprising: providing a first maskpattern that includes a plurality of distinct types of regionscorresponding to the distinct types of regions of the photo-mask; usinga processor to calculate a gradient of a function, wherein the functiondepends on the first mask pattern and an estimate of a wafer patternthat results from the photolithographic process utilizing at least aportion of the first mask pattern, and wherein the gradient iscalculated without determining the function; and generating a secondmask pattern based, at least in part, on the gradient of the function.18. The computer-implemented method of claim 17, wherein the gradientcorresponds to a Frechet derivative.
 19. The computer-implemented methodof claim 17, wherein the gradient is a closed-form expression.
 20. Thecomputer-implemented method of claim 17, wherein the output of thefunction further depends on at least a portion of a target pattern thatis to be printed by the photo-mask during the photolithographic process.21. The computer-implemented method of claim 20, wherein another targetpattern is modified to produce the target pattern.
 22. Thecomputer-implemented method of claim 20, further comprising converting adocument that includes a polygon-based representation of the targetpattern into the target pattern.
 23. The computer-implemented method ofclaim 20, further comprising converting a document that includes anedge-based representation of the target pattern into the target pattern.24. The computer-implemented method of claim 17, wherein the functiondepends, at least in part, on a model of the photolithographic process,wherein the model includes a resist model.
 25. The computer-implementedmethod of claim 24, wherein the resist model includes simulateddevelopment of a resist.
 26. The computer-implemented method of claim17, wherein the generating involves: modifying a first representation ofthe first mask pattern to produce a second representation of the secondmask pattern in accordance with the gradient; and extracting a secondmask pattern from the second representation.
 27. Thecomputer-implemented method of claim 26, wherein the second mask patternis piece-wise rectilinear.
 28. The computer-implemented method of claim26, wherein the first representation is a distance function in which avalue of the first representation corresponds to a distance from anearest contour in a plane of the first mask pattern.
 29. Thecomputer-implemented method of claim 28, wherein the firstrepresentation has a pre-determined value on a contour in the plane. 30.The computer-implemented method of claim 29, further comprisingadjusting the second representation such that it is a distance functionin which a value of the second representation corresponds to a distancefrom a nearest contour in a plane of the second mask pattern.
 31. Thecomputer-implemented method of claim 17, further comprising combiningthe second mask pattern with a third mask pattern, wherein the thirdmask pattern is determined in accordance with pre-determinedoptical-proximity-correction rules.
 32. The computer-implemented methodof claim 17, wherein the function is a merit function that assignsdifferent relative weights to different regions of the first maskpattern.
 33. The computer-implemented method of claim 17, wherein thefunction is a merit function that includes estimate of a process windowcorresponding to the photolithographic process.
 34. Thecomputer-implemented method of claim 17, wherein the function is a meritfunction that includes a model of the photolithographic process thatincludes out-of-focus conditions.
 35. The computer-implemented method ofclaim 17, wherein the function is a merit function that indicates adegree of desirability of the first mask pattern.
 36. Thecomputer-implemented method of claim 17, wherein the gradient iscalculated in accordance with a formula obtained by taking a derivativeof the function.